import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_, drop_path
import math
from functools import partial

import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm

from mmdet.models.builder import BACKBONES
from mmcv.runner import (auto_fp16, force_fp32,)
from mmcv.runner import BaseModule


class ConvStem(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_eval=False):
        super().__init__()

        self.patch_size = patch_size
        self.norm_eval = norm_eval

        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0] // 4, img_size[1] // patch_size[1] // 4]
        self.img_size = img_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        stem_dim = embed_dim // 2
        self.stem = nn.Sequential(
            nn.Conv2d(in_chans, stem_dim, kernel_size=3,
                      stride=2, padding=1, bias=False),
            nn.BatchNorm2d(stem_dim),
            nn.GELU(),
            nn.Conv2d(stem_dim, stem_dim, kernel_size=3,
                      groups=stem_dim, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(stem_dim),
            nn.GELU(),
            nn.Conv2d(stem_dim, stem_dim, kernel_size=3,
                      groups=stem_dim, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(stem_dim),
            nn.GELU(),
            nn.Conv2d(stem_dim, stem_dim, kernel_size=3,
                      groups=stem_dim, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(stem_dim),
            nn.GELU(),
        )
        self.proj = nn.Conv2d(stem_dim, embed_dim,
                              kernel_size=3,
                              stride=2, padding=1)
        self.norm = nn.LayerNorm(embed_dim)

    def forward(self, x):
        stem = self.stem(x)
        x = self.proj(stem)
        _, _, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)
        return x, (H, W)

    def train(self, mode=True):
        """Convert the model into training mode while keep normalization layer
        freezed."""
        super().train(mode)
        if mode and self.norm_eval:
            for m in self.modules():
                if isinstance(m, _BatchNorm):
                    m.eval()


class BiAttn(nn.Module):
    def __init__(self, in_channels, act_ratio=0.25, act_fn=nn.GELU, gate_fn=nn.Sigmoid):
        super().__init__()
        reduce_channels = int(in_channels * act_ratio)
        self.norm = nn.LayerNorm(in_channels)
        self.global_reduce = nn.Linear(in_channels, reduce_channels)
        self.local_reduce = nn.Linear(in_channels, reduce_channels)
        self.act_fn = act_fn()
        self.channel_select = nn.Linear(reduce_channels, in_channels)
        self.spatial_select = nn.Linear(reduce_channels * 2, 1)
        self.gate_fn = gate_fn()

    def forward(self, x):
        ori_x = x
        x = self.norm(x)
        x_global = x.mean(1, keepdim=True)
        x_global = self.act_fn(self.global_reduce(x_global))
        x_local = self.act_fn(self.local_reduce(x))

        c_attn = self.channel_select(x_global)
        c_attn = self.gate_fn(c_attn)  # [B, 1, C]
        s_attn = self.spatial_select(torch.cat([x_local, x_global.expand(-1, x.shape[1], -1)], dim=-1))
        s_attn = self.gate_fn(s_attn)  # [B, N, 1]

        attn = c_attn * s_attn  # [B, N, C]
        return ori_x * attn


class BiAttnMlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.attn = BiAttn(out_features)
        self.drop = nn.Dropout(drop) if drop > 0 else nn.Identity()

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.attn(x)
        x = self.drop(x)
        return x


def window_reverse_lv(
        windows: torch.Tensor,
        original_size,
        window_size=(7, 7)
) -> torch.Tensor:
    """ Reverses the window partition.
    Args:
        windows (torch.Tensor): Window tensor of the shape [B * windows, window_size[0] * window_size[1], C].
        original_size (Tuple[int, int]): Original shape.
        window_size (Tuple[int, int], optional): Window size which have been applied. Default (7, 7)
    Returns:
        output (torch.Tensor): Folded output tensor of the shape [B, original_size[0] * original_size[1], C].
    """
    # Get height and width
    H, W = original_size
    # Compute original batch size
    B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))
    # Fold grid tensor
    output = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1)
    output = output.permute(0, 1, 3, 2, 4, 5).reshape(B, H * W, -1)
    return output


def get_relative_position_index(
        win_h: int,
        win_w: int
) -> torch.Tensor:
    """ Function to generate pair-wise relative position index for each token inside the window.
        Taken from Timms Swin V1 implementation.
    Args:
        win_h (int): Window/Grid height.
        win_w (int): Window/Grid width.
    Returns:
        relative_coords (torch.Tensor): Pair-wise relative position indexes [height * width, height * width].
    """
    coords = torch.stack(torch.meshgrid([torch.arange(win_h), torch.arange(win_w)]))
    coords_flatten = torch.flatten(coords, 1)
    relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
    relative_coords = relative_coords.permute(1, 2, 0).contiguous()
    relative_coords[:, :, 0] += win_h - 1
    relative_coords[:, :, 1] += win_w - 1
    relative_coords[:, :, 0] *= 2 * win_w - 1
    return relative_coords.sum(-1)


class Attention(nn.Module):
    def __init__(self, dim, num_tokens=1, num_heads=8, window_size=7, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.num_tokens = num_tokens
        self.window_size = window_size
        self.attn_area = window_size * window_size
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.kv_global = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop) if attn_drop > 0 else nn.Identity()
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0 else nn.Identity()

        # positional embedding
        # Define a parameter table of relative position bias, shape: 2*Wh-1 * 2*Ww-1, nH
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), num_heads))

        # Get pair-wise relative position index for each token inside the window
        self.register_buffer("relative_position_index", get_relative_position_index(window_size,
                                                                                    window_size).view(-1))
        # Init relative positional bias
        trunc_normal_(self.relative_position_bias_table, std=.02)

    def _get_relative_positional_bias(
            self
    ) -> torch.Tensor:
        """ Returns the relative positional bias.
        Returns:
            relative_position_bias (torch.Tensor): Relative positional bias.
        """
        relative_position_bias = self.relative_position_bias_table[
            self.relative_position_index].view(self.attn_area, self.attn_area, -1)
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
        return relative_position_bias.unsqueeze(0)

    def forward_global_aggregation(self, q, k, v):
        """
        q: global tokens
        k: image tokens
        v: image tokens
        """
        B, _, N, _ = q.shape
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
        return x

    def forward_local(self, q, k, v, H, W):
        """
        q: image tokens
        k: image tokens
        v: image tokens
        """
        B, num_heads, N, C = q.shape
        ws = self.window_size
        h_group, w_group = H // ws, W // ws

        # partition to windows
        q = q.view(B, num_heads, h_group, ws, w_group, ws, -1).permute(0, 2, 4, 1, 3, 5, 6).contiguous()
        q = q.view(-1, num_heads, ws*ws, C)
        k = k.view(B, num_heads, h_group, ws, w_group, ws, -1).permute(0, 2, 4, 1, 3, 5, 6).contiguous()
        k = k.view(-1, num_heads, ws*ws, C)
        v = v.view(B, num_heads, h_group, ws, w_group, ws, -1).permute(0, 2, 4, 1, 3, 5, 6).contiguous()
        v = v.view(-1, num_heads, ws*ws, v.shape[-1])

        attn = (q @ k.transpose(-2, -1)) * self.scale
        pos_bias = self._get_relative_positional_bias()
        attn = (attn + pos_bias).softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(v.shape[0], ws*ws, -1)

        # reverse
        ori_size = (H, W)
        x = window_reverse_lv(x, ori_size, (ws, ws))
        return x

    def forward_global_broadcast(self, q, k, v):
        """
        q: image tokens
        k: global tokens
        v: global tokens
        """
        B, num_heads, N, _ = q.shape
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
        return x

    def forward(self, x, H, W):
        B, N, C = x.shape
        NC = self.num_tokens
        # pad
        x_img, x_global = x[:, NC:], x[:, :NC]
        x_img = x_img.view(B, H, W, C)
        pad_l = pad_t = 0
        ws = self.window_size
        pad_r = (ws - W % ws) % ws
        pad_b = (ws - H % ws) % ws
        x_img = F.pad(x_img, (0, 0, pad_l, pad_r, pad_t, pad_b))
        Hp, Wp = x_img.shape[1], x_img.shape[2]
        x_img = x_img.view(B, -1, C)
        x = torch.cat([x_global, x_img], dim=1)

        # qkv
        qkv = self.qkv(x)
        q, k, v = qkv.view(B, -1, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).unbind(0)

        # split img tokens & global tokens
        q_img, k_img, v_img = q[:, :, NC:], k[:, :, NC:], v[:, :, NC:]
        q_cls, _, _ = q[:, :, :NC], k[:, :, :NC], v[:, :, :NC]

        # local window attention
        x_img = self.forward_local(q_img, k_img, v_img, Hp, Wp)
        # restore to the original size
        x_img = x_img.view(B, Hp, Wp, -1)[:, :H, :W].reshape(B, H*W, -1)
        q_img = q_img.reshape(B, self.num_heads, Hp, Wp, -1)[:, :, :H, :W].reshape(B, self.num_heads, H*W, -1)
        k_img = k_img.reshape(B, self.num_heads, Hp, Wp, -1)[:, :, :H, :W].reshape(B, self.num_heads, H*W, -1)
        v_img = v_img.reshape(B, self.num_heads, Hp, Wp, -1)[:, :, :H, :W].reshape(B, self.num_heads, H*W, -1)

        # global aggregation
        x_cls = self.forward_global_aggregation(q_cls, k_img, v_img)
        k_cls, v_cls = self.kv_global(x_cls).view(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).unbind(0)

        # gloal broadcast
        x_img = x_img + self.forward_global_broadcast(q_img, k_cls, v_cls)

        x = torch.cat([x_cls, x_img], dim=1)
        x = self.proj(x)
        return x


class Block(nn.Module):

    def __init__(self, dim, num_heads, num_tokens=1, window_size=7, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attention=Attention, last_block=False):
        super().__init__()
        self.last_block = last_block
        self.norm1 = norm_layer(dim)
        self.attn = attention(dim, num_heads=num_heads, num_tokens=num_tokens, window_size=window_size, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = BiAttnMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        if self.last_block:
            # ignore unused global tokens in downstream tasks
            x = x[:, -H*W:]
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class ResidualMergePatch(nn.Module):
    def __init__(self, dim, out_dim, num_tokens=1):
        super().__init__()
        self.num_tokens = num_tokens
        self.norm = nn.LayerNorm(4 * dim)
        self.reduction = nn.Linear(4 * dim, out_dim, bias=False)
        self.norm2 = nn.LayerNorm(dim)
        self.proj = nn.Linear(dim, out_dim, bias=False)
        # use MaxPool3d to avoid permutations
        self.maxp = nn.MaxPool3d((2, 2, 1), (2, 2, 1))
        self.res_proj = nn.Linear(dim, out_dim, bias=False)

    def forward(self, x, H, W):
        global_token, x = x[:, :self.num_tokens].contiguous(), x[:, self.num_tokens:].contiguous()
        B, L, C = x.shape

        x = x.view(B, H, W, C)
        # pad
        pad_l = pad_t = 0
        pad_r = (2 - W % 2) % 2
        pad_b = (2 - H % 2) % 2
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))

        res = self.res_proj(self.maxp(x).view(B, -1, C))

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)
        x = x + res
        global_token = self.proj(self.norm2(global_token))
        x = torch.cat([global_token, x], 1)
        return x, (math.ceil(H / 2), math.ceil(W / 2))

class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops


class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)
        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
        else:
            shifted_x = x
        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)
        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops


class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm, num_tokens=8):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.num_tokens = num_tokens
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.token_reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        x_tokens = x[:, :self.num_tokens, :]
        x = x[:, self.num_tokens:, :]
        H, W = self.input_resolution[0] // 4, self.input_resolution[1] // 4
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)
        x = torch.cat((x_tokens, x), dim=1)
        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


class PatchExpand(nn.Module):
    def __init__(self, input_resolution, dim, dim_scale=2, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.expand = nn.Linear(dim, 2 * dim, bias=False) if dim_scale == 2 else nn.Identity()
        self.norm = norm_layer(dim // dim_scale)

    def forward(self, x, out_H, out_W):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        H = H
        W = W
        x = self.expand(x)  # (49, 768) -> (49, 1536)
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)  # (49, 1536) -> (7, 7, 1536)
        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=2, p2=2, c=C // 4)  # (7, 7, 1536) -> (14, 14, 384)
        x = x.view(B, -1, C // 4)  # (14, 14, 384) -> (196, 384)
        x = self.norm(x)

        return x, out_H * 2, out_W * 2


class FinalPatchExpand_X4(nn.Module):
    def __init__(self, input_resolution, dim, dim_scale=4, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.dim_scale = dim_scale
        self.expand = nn.Linear(dim, 16 * dim, bias=False)
        self.output_dim = dim
        self.norm = norm_layer(self.output_dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        x = self.expand(x)  # (3136, 96) -> (3136, 1536)
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)  # (3136, 1536) -> (56, 56, 1536)
        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
                      c=C // (self.dim_scale ** 2))  # (56, 56, 1536) -> (224, 224, 96)
        x = x.view(B, -1, self.output_dim)  # (224, 224, 96) -> (50176, 96)
        x = self.norm(x)

        return x


class FinalPatchExpand_X2(nn.Module):
    def __init__(self, input_resolution, dim, dim_scale=4, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.dim_scale = dim_scale
        self.expand = nn.Linear(dim, 4 * dim, bias=False)
        self.output_dim = dim
        self.norm = norm_layer(self.output_dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        x = self.expand(x)  # (3136, 96) -> (3136, 1536)
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)  # (3136, 1536) -> (56, 56, 1536)
        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
                      c=C // (self.dim_scale ** 2))  # (56, 56, 1536) -> (224, 224, 96)
        x = x.view(B, -1, self.output_dim)  # (224, 224, 96) -> (50176, 96)
        x = self.norm(x)

        return x

class FinalPatchExpand_X8(nn.Module):
    def __init__(self, input_resolution, dim, dim_scale=4, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.dim_scale = dim_scale
        self.expand = nn.Linear(dim, 64 * dim, bias=False)
        self.output_dim = dim
        self.norm = norm_layer(self.output_dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        x = self.expand(x)  # (3136, 96) -> (3136, 1536)
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)  # (3136, 1536) -> (56, 56, 1536)
        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
                      c=C // (self.dim_scale ** 2))  # (56, 56, 1536) -> (224, 224, 96)
        x = x.view(B, -1, self.output_dim)  # (224, 224, 96) -> (50176, 96)
        x = self.norm(x)

        return x

class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, num_tokens=8):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            # SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
            #                      num_heads=num_heads, window_size=window_size,
            #                      shift_size=0 if (i % 2 == 0) else window_size // 2,
            #                      mlp_ratio=mlp_ratio,
            #                      qkv_bias=qkv_bias, qk_scale=qk_scale,
            #                      drop=drop, attn_drop=attn_drop,
            #                      drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
            #                      norm_layer=norm_layer)
            Block(
                dim=dim, num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                norm_layer=norm_layer, attention=Attention, num_tokens=8)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(dim, dim * 2, num_tokens=num_tokens)
        else:
            self.downsample = None

    def forward(self, x, H, W):
        # 流经SwinTransformerBlock
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x, H, W)
        if self.downsample is not None:
            x, (H, W) = self.downsample(x, H, W)
        return x, H, W

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops


class BasicLayer_up(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        upsample (nn.Module | None, optional): Upsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size, num_tokens=8,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, upsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint
        self.num_tokens = num_tokens

        # build blocks
        self.blocks = nn.ModuleList([
            # SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
            #                      num_heads=num_heads, window_size=window_size,
            #                      shift_size=0 if (i % 2 == 0) else window_size // 2,
            #                      mlp_ratio=mlp_ratio,
            #                      qkv_bias=qkv_bias, qk_scale=qk_scale,
            #                      drop=drop, attn_drop=attn_drop,
            #                      drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
            #                      norm_layer=norm_layer)
            Block(
                dim=dim, num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                norm_layer=norm_layer, attention=Attention, num_tokens=8)
            for i in range(depth)])

        # patch merging layer
        if upsample is not None:
            self.upsample = PatchExpand(input_resolution, dim=dim, dim_scale=2, norm_layer=norm_layer)
        else:
            self.upsample = None

    def forward(self, x, H, W):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x, H, W)
        if self.upsample is not None:
            x = x[:, self.num_tokens:, :]
            x, H, W = self.upsample(x, H, W)
        return x, H, W


class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape  # (3, 224, 224)
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)  # (3, 224, 224) -> (96, 56, 56) -> (96, 3136) -> (3136, 96)
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class SwinTransformerSys(nn.Module):
    r""" Swin Transformer
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=4, num_classes=3,
                 embed_dim=96, depths=[2, 2, 2, 2], depths_decoder=[1, 2, 2, 2], num_heads=[3, 6, 12, 24],
                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, num_tokens=8,
                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                 use_checkpoint=False, final_upsample="expand_first", **kwargs):
        super().__init__()

        print(
            "SwinTransformerSys expand initial----depths:{};depths_decoder:{};drop_path_rate:{};num_classes:{}".format(
                depths,
                depths_decoder, drop_path_rate, num_classes))
        self.patch_size = patch_size
        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.num_tokens = num_tokens
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.num_features_up = int(embed_dim * 2)
        self.mlp_ratio = mlp_ratio
        self.final_upsample = final_upsample

        # split image into non-overlapping patches
        self.patch_embed = ConvStem(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_eval=False)
        # self.patch_embed = PatchEmbed(
        #     img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
        #     norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build encoder and bottleneck layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                 patches_resolution[1] // (2 ** i_layer)),
                               depth=depths[i_layer],
                               num_heads=num_heads[i_layer],
                               window_size=window_size,
                               mlp_ratio=self.mlp_ratio,
                               qkv_bias=qkv_bias, qk_scale=qk_scale,
                               drop=drop_rate, attn_drop=attn_drop_rate,
                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                               norm_layer=norm_layer,
                               downsample=ResidualMergePatch if (i_layer < self.num_layers - 1) else None,
                               # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                               use_checkpoint=use_checkpoint,
                               num_tokens=self.num_tokens)
            self.layers.append(layer)

        # build decoder layers
        self.layers_up = nn.ModuleList()
        self.concat_back_dim = nn.ModuleList()
        for i_layer in range(self.num_layers):
            concat_linear = nn.Linear(2 * int(embed_dim * 2 ** (self.num_layers - 1 - i_layer)),
                                      int(embed_dim * 2 ** (
                                              self.num_layers - 1 - i_layer))) if i_layer > 0 else nn.Identity()
            if i_layer == 0:
                layer_up = PatchExpand(
                    input_resolution=(patches_resolution[0] // (2 ** (self.num_layers - 1 - i_layer)),
                                      patches_resolution[1] // (2 ** (self.num_layers - 1 - i_layer))),
                    dim=int(embed_dim * 2 ** (self.num_layers - 1 - i_layer)), dim_scale=2, norm_layer=norm_layer)
            else:
                layer_up = BasicLayer_up(dim=int(embed_dim * 2 ** (self.num_layers - 1 - i_layer)),
                                         input_resolution=(
                                             patches_resolution[0] // (2 ** (self.num_layers - 1 - i_layer)),
                                             patches_resolution[1] // (2 ** (self.num_layers - 1 - i_layer))),
                                         depth=depths[(self.num_layers - 1 - i_layer)],
                                         num_heads=num_heads[(self.num_layers - 1 - i_layer)],
                                         window_size=window_size,
                                         mlp_ratio=self.mlp_ratio,
                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
                                         drop=drop_rate, attn_drop=attn_drop_rate,
                                         drop_path=dpr[sum(depths[:(self.num_layers - 1 - i_layer)]):sum(
                                             depths[:(self.num_layers - 1 - i_layer) + 1])],
                                         norm_layer=norm_layer,
                                         upsample=PatchExpand if (i_layer < self.num_layers - 1) else None,
                                         use_checkpoint=use_checkpoint,
                                         num_tokens=self.num_tokens)
            self.layers_up.append(layer_up)
            self.concat_back_dim.append(concat_linear)

        self.norm = norm_layer(self.num_features)
        self.norm_up = norm_layer(self.embed_dim)

        if self.final_upsample == "expand_first":
            print("---final upsample expand_first---")
            self.upx4 = FinalPatchExpand_X4(input_resolution=(img_size // patch_size, img_size // patch_size),
                                          dim_scale=2, dim=embed_dim)
            self.upx2 = FinalPatchExpand_X2(input_resolution=(img_size // patch_size, img_size // patch_size),
                                          dim_scale=2, dim=embed_dim)
            self.upx8 = FinalPatchExpand_X8(input_resolution=(img_size // 8, img_size // 8),
                                            dim_scale=2, dim=embed_dim)
            self.output = nn.Conv2d(in_channels=embed_dim, out_channels=self.num_classes, kernel_size=1, bias=False)

        self.apply(self._init_weights)
        self.global_token = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim))
        self.H = None
        self.W = None

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    # Encoder and Bottleneck
    def forward_features(self, x):
        x, (self.H, self.W) = self.patch_embed(x)  # (3, 224, 224) -> (3136, 96)
        global_token = self.global_token.expand(x.shape[0], -1, -1)
        x = torch.cat((global_token, x), dim=1)

        # 绝对位置嵌入
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)
        x_downsample = []

        for layer in self.layers:
            x_downsample.append(x)
            x, self.H, self.W = layer(x, self.H, self.W)

        x = self.norm(x)  # B L C

        return x, x_downsample

    # Dencoder and Skip connection
    def forward_up_features(self, x, x_downsample):
        x_tokens_downsample = []
        for i in range(len(x_downsample)):
            x_tokens_downsample.append(x_downsample[i][:, :self.num_tokens, :])
        for i in range(len(x_downsample)):
            x_downsample[i] = x_downsample[i][:, self.num_tokens:, :]
        for inx, layer_up in enumerate(self.layers_up):
            if inx == 0:
                x = x[:, self.num_tokens:, :]
                x, self.H, self.W = layer_up(x, self.H, self.W)
            else:
                x = torch.cat([x, x_downsample[2 - inx]], -1)
                x = self.concat_back_dim[inx](x)
                x = torch.cat([x_tokens_downsample[2 - inx], x], 1)
                x, self.H, self.W = layer_up(x, self.H, self.W)

        x = self.norm_up(x)  # B L C

        return x

    def up_x4(self, x):
        H, W = self.patches_resolution
        B, L, C = x.shape
        assert L == H * W, "input features has wrong size"

        if self.final_upsample == "expand_first":
            x = self.upx4(x)  # (3136, 96) -> (50176, 96)
            x = x.view(B, 4 * H, 4 * W, -1)  # (50176, 96) -> (224, 224, 96)
            x = x.permute(0, 3, 1, 2)  # B,C,H,W (224, 224, 96) -> (96, 224, 224)
            x = self.output(x)  # (96, 224, 224) -> (9, 224, 224)

        return x

    def up_x2(self, x):
        x = x[:, self.num_tokens:, :]
        H, W = self.patches_resolution
        B, L, C = x.shape
        assert L == H * W, "input features has wrong size"

        if self.final_upsample == "expand_first":
            x = self.upx2(x)  # (3136, 96) -> (50176, 96)
            x = x.view(B, 2 * H, 2 * W, -1)  # (50176, 96) -> (224, 224, 96)
            x = x.permute(0, 3, 1, 2)  # B,C,H,W (224, 224, 96) -> (96, 224, 224)
            x = self.output(x)  # (96, 224, 224) -> (9, 224, 224)

        return x

    def up_x8(self, x):
        x = x[:, self.num_tokens:, :]
        H, W = self.patches_resolution
        B, L, C = x.shape
        assert L == H * W, "input features has wrong size"

        if self.final_upsample == "expand_first":
            x = self.upx8(x)  # (3136, 96) -> (50176, 96)
            x = x.view(B, 8 * H, 8 * W, -1)  # (50176, 96) -> (224, 224, 96)
            x = x.permute(0, 3, 1, 2)  # B,C,H,W (224, 224, 96) -> (96, 224, 224)
            x = self.output(x)  # (96, 224, 224) -> (9, 224, 224)

        return x

    def forward(self, x):
        x, x_downsample = self.forward_features(x)
        x = self.forward_up_features(x, x_downsample)
        x = self.up_x8(x)

        return x

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
        flops += self.num_features * self.num_classes
        return flops
